The extent to which the transition to agriculture in Europe was the result of biological (demic) diffusion from the Near East or the adoption of farming practices by indigenous hunter–gatherers is subject to continuing debate. Thus far, archaeological study and the analysis of modern and ancient European DNA have yielded inconclusive results regarding these hypotheses. Here we test these ideas using an extensive craniometric dataset representing 30 hunter–gatherer and farming populations. Pairwise population craniometric distance was compared with temporally controlled geographical models representing evolutionary hypotheses of biological and cultural transmission. The results show that, following the physical dispersal of Near Eastern/Anatolian farmers into central Europe, two biological lineages were established with limited gene flow between them. Farming communities spread across Europe, while hunter–gatherer communities located in outlying geographical regions adopted some cultural elements from the farmers. Therefore, the transition to farming in Europe did not involve the complete replacement of indigenous hunter–gatherer populations despite significant gene flow from the Southwest Asia. This study suggests that a mosaic process of dispersal of farmers and their ideas was operating in outlying regions of Europe, thereby reconciling previously conflicting results obtained from genetic and archaeological studies.

Their results are remarkably neat, showing two clearly distinct lineages, with comparatively little inter-mixture, confirming the picture from the archaeology of the LBK, for example, which seems to indicate that farmers and foragers kept to their own zones.

This helps to explain why the presumed Neolithic Y-DNA haplogroups G, E and J do not dominate Europe today, and decline in frequency the further one moves from the Mediterranean. The farming pioneers in Europe, though initially successful, eventually encountered problems which led to population crashes. Then after the Neolithic, Europe had two great bursts of migration, both from fringe regions where farming had been adopted by foragers. One came from the European steppe in the Copper and Bronze Ages. The other was the spread of their Germanic and Slavic descendants in the Migration Period.

On the second-to-last page of The $1,000 Genome, Kevin Davies recounts noticing two small children running around at the GET (Genomes, Environments, Traits) Conference:

They turned out to be [. . .] the children of Jong Bhak, the director of the Personal Genomics Institute in Korea, and his American (Caucasian) wife. They were also the youngest members of the genome sequencing club. "We wanted to know how different two siblings can be," said Bhak. "Our hybrid kids can give us some easy confirmation on that. They are brothers, but their genetic makeup will be much more different from each other than any two random people in the same population."

Nothing says "family" like "more different from each other than any two random people in the same population." In fairness, I'm not sure if Bhak is attempting to celebrate this fact, or merely acknowledging a reality that the vast majority of multiracialists no doubt fail to grasp.

We are pleased to tell you that we have just submitted our first scientific paper about the project. The main function of this paper is to announce PoBI to the scientific world and in it we show that, even with a relatively small number of samples and a few genetic markers, the samples we collected should be sufficient to detect genetic differences across the UK. [. . .]

One aspect that is of particular interest is the surnames we collected and we have spent some time with our collaborators at UCL (Professor Paul Longley and his group) dividing them into local and non-local surnames. The figure on the left shows a couple of examples. The idea is that individuals whose surname is local to an area are more likely to have family in that area for many generations than individuals whose surnames are found all over the country. This is obviously a generalisation, but it does seem that there are some genetic differences between sets of volunteers with local surnames and sets with non-local surnames and we are really looking forward to analysing all the data rather than just the small subset we have been studying so far. [. . .]

Our next priority is to analyse the 1.3 million genetic markers that have been typed on 3,000 of our volunteers [. . .] The data we analyse from these samples should shed light on the genetic impact of the different historical incursions into Britain. It is an extremely large data set and so it will take a while to analyse and write up. As mentioned in our last newsletter, 100 of our samples are having their complete DNA sequenced by the 1,000 Genomes Project (www.1000genomes.org) and it should not be too long before that very valuable information becomes available to us. [. . .]

As you will know from the last newsletter, the Wellcome Trust has given us funding for a further five years to look for genes involved in normal traits. The main focus is on facial features, but other traits include handedness, taste perception and skin colour. We have been going back to our volunteers to collect these data. We take 3D photographs of each volunteer’s face in order to identify genes involved in the control of particular facial features. Over the last 18 months, we have collected 475 such photographs and are beginning to analyse them with our collaborators in Surrey (Professor Josef Kittler and his group).

There is a great deal of interest in the genetics of facial features and, in addition, the frequency of genetic variants for facial features may well differ significantly between different parts of the UK. We will also be collecting data on a variety of other normal features including height, hair and skin colour, handedness, milk tolerance, musical preferences and perfect pitch, taste and smell preferences and features of the hand.

"Our findings suggest that recent human adaptation has not taken place through the arrival and spread of single changes of large effect, but through shifts of frequency in many places of the genome," co-senior author Mary Przeworski, a human genetics, ecology, and evolution researcher at the University of Chicago, said in a statement. "It suggests that human adaptation, like most common human diseases, has a complex genetic architecture."

Efforts to identify the genetic basis of human adaptations from polymorphism data have sought footprints of “classic selective sweeps” (in which a beneficial mutation arises and rapidly fixes in the population).Yet it remains unknown whether this form of natural selection was common in our evolution. We examined the evidence for classic sweeps in resequencing data from 179 human genomes. As expected under a recurrent-sweep model, we found that diversity levels decrease near exons and conserved noncoding regions. In contrast to expectation, however, the trough in diversity around human-specific amino acid substitutions is no more pronounced than around synonymous substitutions. Moreover, relative to the genome background, amino acid and putative regulatory sites are not significantly enriched in alleles that are highly differentiated between populations. These findings indicate that classic sweeps were not a dominant mode of human adaptation over the past ~250,000 years.

[. . .]

This conclusion does not imply that humans have experienced few phenotypic adaptations, or that adaptations have not shaped genomic patterns of diversity. Comparisons of diversity and divergence levels at putatively functional versus neutral sites, for example, suggest that 10 to 15% [and possibly as many as 40% (29)] of amino acid differences between humans and chimpanzees were adaptive [e.g., (30)], as were 5% of substitutions in conserved noncoding regions (22, 29) and ~20% in UTRs (22). Given the paucity of classic sweeps revealed by our findings, an excess of functional divergence would point to the importance of other modes of adaptation. One way to categorize modes of adaptation is in terms of their effect on the allele frequencies at sites that affect the beneficial phenotype. In this view, classic sweeps bring new alleles to fixation; selection on standing variation or on multiple beneficial alleles brings rare or intermediate frequency alleles to fixation; and other forms of adaptation, such as selection on polygenic traits, increase or decrease allele frequencies to a lesser extent. Such changes in allele frequencies can decrease variation at closely linked sites—to a lesser extent than in a full sweep—and might therefore contribute to a reduction in diversity near functional elements (31) as well as to excess divergence. Alternatives to classic sweeps are likely for parameters applicable to human populations (7, 32); in particular, many phenotypes of interest are quantitative and plausibly result from selection at many loci of small effect (8).

An important implication is that in the search for targets of human adaptation, a change in focus is warranted. To date, selection scans have relied almost entirely on the sweep model, either explicitly (by considering strict neutrality as the null hypothesis and a classic sweep as the alternative) or implicitly (by ranking regions by a statistic thought to be sensitive to classic sweeps and focusing on the tails of the empirical distribution). It appears that few adaptations in humans took the form that these approaches are designed to detect, such that low-hanging fruits accessible by existing approaches may be largely depleted. Conversely, the more common modes of adaptation likely remain undetected. Thus, to dissect the genetic basis of human adaptations and assess what fraction of the genome was affected by positive selection, we need new tests to detect other modes of selection, such as comparisons between closely related populations that have adapted to drastically different environments [e.g., (33)] or methods that consider loci that contribute to the same phenotype jointly [e.g., (34)]. Moreover, if alleles that contribute to recent adaptations are often polymorphic within a population, genome-wide association studies should be highly informative.